Simulation for Domestic Load Forecasting

DOI : 10.17577/IJERTCONV10IS11078

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Simulation for Domestic Load Forecasting

Nandish B M

Dept. Electrical and Electronics Jain Institute of Technology Davangere, India

Shashank N S

Dept. Electrical and Electronics Engineering

Jain Institute of Technology Davangere, India

Praveen H K

Dept. Electrical and Electronics Engineering

Jain Institute of Technology Davangere, India

Govindappa C G

Dept. Electrical and Electronics Engineering Jain Institute of Technology

Davangere, India

Sudarshan D S

Dept. Electrical and Electronics Engineering Jain Institute of Technology

Davangere, India

Abstract Some long-time period layout duties depend upon electric load forecasting, and forecasting mistakes cause destiny choices which might be incorrect. The technique primarily based totally on enlarged particular indexes (ESI), the econometric technique, and the self-enough technique are the 3 maximum appreciably used forecasting strategies today. All of those have their very own drawbacks and estimating flaws. This studies tries to perceive a sphere of utility for every of these methodologies and set up a mixed utility precept in order that forecasting mistakes may be reduced. A new technique is offered, primarily based totally on calculations, with suggestions for all modern methods primarily based totally at the calculations and the manner that the mixed utility of diverse strategies become brought to reinforce the efficiency.

Keywords Artificial Neural Network (ANN), -Red Data Set (RDD set), National electricity plan (NEP),- Fuzzy Logic (FL)

Fig 1: Energy forecasting classification in accordance with time interval


    Because of the worldwide preference for electrical energy, electrification is a fast-moving process. The demand for power tends to expand faster than the actual generation during economic expansion. The rise is attributed to a number of factors, including:

    • Increase in population Increase in per capita income

    • Urban area mitigation

    • Increase in product energy usage.

    Power system planning begins with a forecast of expected future demand and energy requirements. Demand projections are used to establish the generating transmission and distribution system's capacity, while energy forecasts are used to define the sort of generation facilities necessary. A load forecast can be important in determining capital equipment and fuel procurement policies.

    Forecast are more than simple extrapolation of the post data into future using mathematical formula or gathering the trends from the expert. Forecast the mechanism of arriving at the measures for planning the future. when the forecasting is done correctly, they provide an audit real and a measure of their accuracy. Forecasting helps in planning as well as saving in the cast.

    Short term forecasting: The forecasting has no future out than 48 hours and are commonly used for operational use cases. Those models use real time and near present data, they are less sensitive to seasonal patterns or long-term consumption trends.

    Long term forecasting: The models are usually from one week to multiple years and are commonly used for planning and investment use cases. They are rely on minimum of 3 years of quality data where they extract seasonality patterns and male use of external predictors.


    The several methods have already been implemented in simulation. Some are failed some are intermediate in results and few are success and nearer to predicated results. For the simulation of the domestic load. Several techniques have been used namely: –

    Artificial Neural Network (ANN), Red Data Set (RDD set), Revision Methods, Load Disaggregation methods etc.,

    The neural networks methods meeting time is very slow and it has the major disadvantage that is, it cant place to process several numbers of the data and large number of the data on time which is considerable fault. Wherever the fuzzy logic

    can able to withstand for such consist of the process and helpful in making the system easy, relabel operative friendly. The fuzzy logic delas with high extent parameters such has the detailed investigation and the process of the knowledge base. The load demand can depends upon whether day type, season, temperature, humidity etc, So the above factors decides whether the load forecasting need to done or not. And it also decides that duration of the load forecasting.

    The fuzzy logic helps to each with the variation between the electrical load and the parameters which one distributing it the by the load forecasting. The decides few much load need to be forecasting

    1. The fuzzy logic it facilities operation, can be all As human operating network, network it some how resembles the human division making with the capacity of the generating the solution for the certain or approximate information.

    Fig 2: Basic configuration of fuzzy logic system

    Thus, obviously relying just upon financial measures to survey the proficiency of venture drives is erroneous. Non- monetary components (qualities), which comprise pivotal pointers in the assessment cycle, should be viewed as in the evaluation interaction. This is the main thought.

    The second part that ought to be laid out during the time spent assessing the adequacy of venture projects depends on non-monetary elements that mirror the task's effect on the outer climate, as well as the effect of the outer climate on the undertaking's achievability. Clearly, this gauge should be quantifiable. What's more, in the present unsure world, this should be possible on fluffy scales, which will be great. The utilization of programming products has turned into an undeniable truth with regards to add up to digitalization of all areas of action in the public arena. We utilize the Matlab programming climate to take care of this issue, which incorporates the Fuzzy Logic Toolbox, which has apparatuses for working with fluffy sets.

    The foundation of a solitary major premise of noneconomic boundaries (NEP) to survey the progress of worldwide and public speculation tasks ought to be the third piece of upgrading the elective procedure for evaluating the viability of venture projects.

    As per the proposition, the NEP premise will be utilized to gauge the adequacy of all worldwide and public ventures (optimality), it is assumed that it is brought together. This establishment was laid throughout our examination (see

    Figure 1). The embodiment of the circumstance The pith of the created base ought to be uncovered. boundaries of assessment, which ought to mirror the essential examination discoveries in all circles of life, or in any event, around the world centered Non-financial boundaries are consequently consolidated in the examination. The assessment methodology ought to consider the main interests of the global local area, the local area of a particular nation and district, which are consolidated into a solitary vital improvement


    The following strategies were utilized to construct a mechanism (mode) for accounting for non-economic characteristics in fuzzy scales.

    1. The desirability characteristic approach of Harrington. The method is designed for use to calculate the vital evaluation criterion with the aid of using changing the project's non-financial and financial features, that have awesome bodily entities and magnitudes, right into a unmarried dimensionless scale. This allows us to "minimize" and verify the anticipated funding poject's admissibility for tough formalized non-financial traits and financial factors, in addition to statistically examine it to different funding initiatives.

    2. Software referred to as Matlab. It lets in us to programme the method as an entire and simplicity the selection maker's implementation of the produced method to mix the show of evaluation findings to programme the created mathematical gear for measuring the performance of funding initiatives of worldwide and countrywide importance degree broaden the appropriate interface for a software program script.

    3. Toolkit for Matlab Fuzzy Logic. This toolbox allows us to speedy and successfully outline expected parameters withinside the shape of fuzzy units and translate their received values into favored Harrington desirability scale values the usage of DM rules.


Fig 3: Bar graph for Demand histogram

The demand histogram is depicted in the above figure as a bar graph, which shows the number of data points that fall inside a specified range of values known as "bins,"

indicating the visual interpretation of numerical data. The histogram in figure '3' depicts the demand vs price relationship.

Fig 4: Bar graph for Temperature histogram

The numerical count or percentage of occurrences in the data for each column is represented by the range of outcomes into columns along the x-axis and y-axis, and can be used to illustrate data distributions. The graph of the Temperature histogram versus the quantity or price was plotted in Figure '4' above.

Fig 5: Price histogram

The figure 5 is plotted as the price histogram which represents the graph of price versus the needful demand.

Fig 6: Testing data with and without outlier

The figure 6 is plotted as the price histogram Testing data with and without outlier which represents the graph of price versus the needful demand. Similarly, the figure 6 is the tested reading graph with data outliers and without data outliers.

Fig 7: Predicted price versus Tested price

The figure 7 schematically represents the comparison of the predicted price versus the tested price or the price depends upon the outputs.


The forecasting inaccuracy is considerably reduced in this research by raising the weight of the most comparable days. We also spoke about how to choose similar days in situations where there aren't any. At the same time, due to weather variations and changes in some prominent components, some adjustments to specific qualities must be made over time. For load forecasting, the similar days approach can be combined with other methods such as grey theory for a better outcome.


[1] H.Bauer, R.D. Brandt, J. Jaschke, v. Muller,M. Paulus, Vorhersage des Gasbedarfes, 2001.

[2] J.Campillo, F. Wallin, D. Torstnsson, and I. Vassileva, Energy demand model design for forecasting electricity consumption. Ad simulating demand response scenarios on Sweden. 2001

[3] A. Gudelj, M. M, Krcum, and E.Twrdy, Models and method for operation in port Container Terminals," 2010

[4] D.Steenken, T. Winter, and U.T. Zimmerehmann, Software and Transport optimization in ship Planning, online optimization large. 2001

[5] S. Vob, R. Stahlbock, and D. Steenken, container terminal operation and operation research. Jan 2004

[6] HHLA Hamburger Hafen und logistic AG, Jan 2014

[7] A. Puryaev and A. Puryaev, "Evaluating the Effectiveness of Projects of Global and National Economic Significance Level," vol. 172: Springer, Singapore, 2020, pp. 317-331.

[8] B. Mendecka and J. Koziol, "Application of the method of data reconciliation for minimizing uncertainty of the weight function in the multicriteria optimization model," Archives of Thermodynamics, vol. 36, no. 1, pp. 83-92, Mar 2015, doi: 10.1515/aoter-2015-0006.

[9] Benzheng Xiao Peter G. McLaren, An Artificial Neural Network for Short Term Load Forecasting, 1EEE WESCANEX, Proceedings, pp. 129-133, 1995.

[10] L. M. Saini and M. K. Soni, Artificial neural network based peak load forecasting using Levenberg-Marquardt and quasi-Newton methods, Proc. Inst. Elect. Eng. Generation, Trans. Distrib., vol. 149, no. 5, pp. 578584, Sep. 2002.

[11] Shan Shao and Yaming Sun, "Short-Term Load Forecasting Using Fuzzy Neural Network," in Proc. 1997 Advances in Power System Control, Operation and Management Conf., pp. 131-134.

[12] Sandeep Sachdeva, and Chander Mohan Verma, Load Forecasting using Fuzzy Methods IEEE Fuzzy Sysyems Conf., pp. 978-1762 ,2008.

[13] Riza C. Berkan and Sheldon L. Trubatch, "Fuzzy Systems Design Principles: Building Fuzzy IF-THEN Rule Bases", New York: IEEE Press, p. 201, 1997.

[14] H. Demuth and M. Beale, Neural Network tool box for use with MATLAB, Users guide, Version 4, the Math works Inc. 2001.

[15] B. M. Nandish and V. Pushparajesh, "Simulation of Household Appliances with Energy Disaggrigation using Deep Learning Technique," 2021 International Conference on Computational Performance Evaluation (ComPE), 2021, pp. 173-178, doi: 10.1109/ComPE53109.2021.9752375.

[16] D.Srinivasan, et al. Practical implementation of a hybrid fuzzy neural network for one-day-ahead load forecasting IEE Prof. Gener.Transm Disstrib, 145(6).1998. Matlab Tool box (version 9.2.0)